Simulated Annealing Genetic Algorithm MATLAB Implementation

This MATLAB program implements a hybrid optimization approach combining Simulated Annealing and Genetic Algorithms. The implementation includes detailed explanations of both algorithms' principles and practical implementation methods. The Genetic Algorithm component handles population initialization, selection, crossover, and mutation operations, while the Simulated Annealing module introduces temperature-controlled acceptance criteria to escape local optima. Key implementation features include: - Population management with fitness evaluation functions - Adaptive cooling schedules for simulated annealing - Configurable crossover and mutation rates - Convergence criteria and optimization progress tracking The code provides modular structure with separate functions for genetic operations (selection(), crossover(), mutation()) and annealing processes (acceptance_probability(), update_temperature()). Both algorithms are effective optimization techniques for solving complex problems and function optimization tasks. By studying this implementation, researchers can better understand and apply these hybrid optimization methods to their own projects and research work. The program is available for download and encourages knowledge sharing and experience exchange among users. The implementation demonstrates practical integration of metaheuristic algorithms with clear code documentation for educational purposes.